IA y el futuro del periodismo (un borrador)
Author: Alejandro Piscitelli
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1. EL PULSO DEL MUNDO (Factfullness, periodismo de soluciones y analítica cultural)
1.1 Factfulness
Rossling Why we wrote factfulness
1.2 El pulso del mundo
1.2 OCVI
Jorge Carrion OBJETOS CULTURALES VAGAMENTE IDENTIFICADOS
Un nuevo canon cultural en diez objetos – The New York Times
Esta no es una lista tradicional: es una antología de objetos que conforman, en conjunto, el retrato de una época que ha borrado los límites entre la alta cultura y, por ejemplo, una historia de Instagram.
Pertenecen al ecosistema cultural del siglo XXI, caracterizado por la multiplicación exponencial de prácticas creativas que, al mismo tiempo que atentan contra los límites entre arte y comunicación, generan un nuevo canon a velocidad de vértigo.
He seleccionado diez proyectos (o OCVI) de este año, que insinúan la existencia de un nuevo canon cultural.
10. Una visualización de datos An Alternative Data-Driven Country
9. Una campaña publicitaria Dream Further
Treinta y un años después de lanzar al mundo el ya mítico eslogan «Just do it» de Nike
8. Una lista de reproducción la lista de Spotify «Films & TV Favorites. Quentin Tarantino Takeover»
7. Un tuit (o ‘remix’) todo lo que ha sucedido + la música de «Years and Years».
6. Un proyecto transmedia De barrios somos
5. Una historia interactiva un videojuego experimental: Telling Lies.
4. Un fichero que es libro objeto
La escritora mexicana Vivian Abenshushan es el alma de Permanente alma negra probablemente el libro más extraño y múltiple que se ha publicado este año. El sitio web
3. Una serie para móviles 1968.Digital —pensada para verse en móviles— analiza la tecnología y la cultura de ese año desde la convicción de que fue cuando se crearon los cimientos del mundo como lo conocemos hoy.
2. Un pódcast na antología de alrededor de 150 historias emitidas desde 2012 con muchos autores pero una única personalidad. una red internacional de clubes de escucha (reuniones físicas de grupos de oyentes) Radio Ambulante
1. Una historia de Instagram El objeto cultural que más me ha gustado e interesado este 2019 es la serie que se emitió en mayo en el perfil de Instagram @eva.stories Eva retransmite sus difíciles vivencias en vídeos que incluyen emoticonos, filtros, mensajes.
1.3 Toolkits
Innovación y Barbarie El libro-toolkit de Alonso & Piscitelli (Indice Parte 1 e Introducción)
2. NEWS OF THE FUTURE YESTERDAY (CIENCIA-FICCION Y RETROPROGRESIVIDAD)
Thoughts on the future of news (circa 1996)
The application of technology to the future of news is not only about the efficiency of professional production and distribution of news. It is also about providing the news consumer with tools that facilitate creation, access and use of news in both individual and communal contexts. While the adoption of digital communication technology by the news industry will enhance consumer access to information, it must also support news as a «community service.» News as a service model is one in which the consumer of news is an active, engaged participant. This service model encourages two-way communication between the traditional news provider and the consumer, and communication within communities built upon common interests. The service model of news becomes a part of the social fabric within communities, a catalyst for creating communities of interest, and a means of facilitating community introspection.
Modern telecommunications is leading us inevitably to the smallest news product imaginable: the personalized newspaper, or Daily Me, whose content has been tailored to meet an individual’s needs and interest. Computerized «butlers» or «agents» are acting on the reader’s behalf, culling articles of interest from traditional and non-traditional news sources, before sending them down the wire to the reader’s home. Luddites see the Daily Me engendering a fragmented world populated by self-interested myopes. They argue that editors should continue to publish articles that establish the point-of-view for the community. They want news pushed upon them. The Daily Me proponents want to pull news in.
There are alternative interpretations of the Daily Me. Regardless of whether one subscribes to the «push» or «pull» model of news, such systems can personalize articles for individuals and communities of readers, e.g., varying the degree of detail and background information provided in an article, or reflecting what the community already knows or does not know about the topic. Providing readers with the proper context is as important as providing the content itself.
News organizations must continue to provide news to individuals and encyclopedic knowledge about their communities. But they must also acknowledge the role of consumers as producers. The future of the industry is as much about construction as it is about consumption. The impact of «going digital» is the emergence of a new relationship between publishers and their public: making news more relevant by building linkages between news providers and consumers.
Carlos Martínez Macías Inteligencia Artificial en TV Morfosis
Gabriele de Narrativa, Entrevista a David Llorente
LEO de LeoRobotIA Robots que escriben noticias en español: cuando las máquinas hacen el trabajo de un periodista
y Kuaibi Xiaoxi el reportero-robot de Xinhua. China cambia periodistas por máquinas
Encima Tomasz Schafernaker, de la BBC corregido por Siri.
Narrativa, una opción para automatizar la generación de contenidos
Informe IF y Reuters
Francesco Marconi Editorial Lab WSJ
La agencia que distribuye alrededor de tres millones de noticias al año a dos mil medios de comunicación en al menos 180 ciudades de 120 países, contará con un sistema que permitirá filtrar información y generar contenidos de audiencias de nicho. https://www.milenio.com/opinion/carlos-martinez-macias/sin-pedir-audiencia/inteligencia-artificial-en-tv-morfosis
https://www.researchgate.net/publication/282642995_Mapping_the_field_of_Algorithmic_Journalism
https://en.wikipedia.org/wiki/Automated_journalism
https://www.techemergence.com/automated-journalism-applications/
https://datajournalism.com/read/longreads/the-dos-and-donts-of-predictive-journalism
https://www.youtube.com/watch?v=1nJhe0XKfhc
https://emerj.com/ai-sector-overviews/automated-journalism-applications/
Editor The New York Times – Semantic Discovery, Comment Monitoring
Editor, an experiment in publishing from NYT R&D on Vimeo.
Juicer BBC News Labs – Semantic Discovery
Reuters – Data Visualization
https://www.graphiq.com
The Washington Post – Automated Journalism
Washington Post to Cover Every Major Race on Election Day With Help of Artificial Intelligence
Heliograf, a data-crunching program built in-house, will automatically update stories as results roll in on Election Day
https://www.wsj.com/articles/washington-post-to-cover-every-major-race-on-election-day-with-help-of-artificial-intelligence-1476871202
Yahoo! Sports – Automated Journalism
3. IA DEL MITO A LA REALIDAD Y VUELTA
3.1 Programas de Investigacion en IA
3.2 Grados de automatización del periodismo
3.2.1 Journalism and artificial intelligence: some notes
Automated Journalism – AI Applications at New York Times, Reuters, and Other Media Giants | Emerj
Artificial intelligence and machine learning can help the news media with its three core problems:
1. The overabundance of information and sources that leave the public confused
2. The credibility of journalism in a world of disinformation and falling trust and literacy
3. The Business model crisis – how can journalism become more efficient – avoiding duplication; be more engaged, add value and be relevant to the individual’s and communities’ need for quality, accurate information and informed, useful debate.
Some caveats about using AI in journalism:
1. Narratives are difficult to program. Trusted journalists are needed to understand and write meaningful stories.
2. Artificial Intelligence needs human inputs. Skilled journalists are required to double check results and interpret them.
3. Artificial Intelligence increases quantity, not quality. It’s still up to the editorial team and developers to decide what kind of journalism the AI will help create.
Here are a few applications that I think are interesting:
• Curating the abundance of data – finding stories eg through Trending Topics
• Responding to instant news (breaking news)
• Monitoring – eg during terror incidents
• Producing ‘robot’ news for basic reporting (eg financial services/weather etc)
• Reducing duplication -(Kaleida trending news)
• Helping with fact-checking (FullFact live AI verification)
• Verification (esp on platforms) to identify fake news – hate speech and to counter bots – not easy as we saw with recent YouTube ‘crisis actors’ problems – it can be gamed.
• Personalisation of journalism eg Compass News app – gives you more specialised, diverse, serendipitous curation;
• Editorial planning (Chartbeat, Orphan)
• Marketing (WSJ, FT subscription efforts)
• Data mining for investigative journalism, relevance mining eg local news
• New platform opportunities such as voice/Alexa/Google assistant – or Augmented Reality – and of course, blockchain
Of course – like any technological change there are going to be negatives as well as positives:
• Automated journalism still needs to be edited by humans
• Verification at its most important is always humanly complex
• Platforms find it difficult to us AI at scale and at speed and in detail (YouTube Crisis Actors) and can be gamed
• Marketing – how do we use AI to find new people not just to track a core readership – how do we use it to find underserved communities
• Personalisation – how do we use AI to provide diversity not just favourites
• Discovery – data sets are often very bad – eg court records in the UK
• Blockchain – really interesting work being done on decentralised content creation and dissemination but can you scale it and make it useful in real time and in a news cycle?
Then there are the broader structural issues around this profound shift to a new tech paradigm:
1. Does mainstream journalism have the skills and insights to make the most of the changes? Are savings ploughed back into ‘real’ human journalism?
2. Trust and transparency – there are a new set of ethical dilemmas that need to be addressed – with AI how do we know who has created content and the sources? How do we hold them accountable? How do you even know it’s a machine?
3. Plus the usual algorithmic biases of gender and the dangers of tech companies and developers gaining power at the expense of the journalists or the public. There’s nothing innately democratic or progressive about AI.
3.2.2 The dos and don’ts of predictive journalism
Michael Lewis writes in The Undoing Project, his best-selling biography of Nobel prize winner Daniel Kahneman and Amos Tversky, that «knowledge is prediction«.
Most major media outlets now have dedicated data journalism teams to model data and create stories driven by prediction.
Modelling data also gives way to data visualisation, a useful journalistic tool in the age of digital media. It makes sense that predictive journalism has gone mainstream.
Outlets now forecast everything from
the Oscars 2018: Here Are Our Final Predictions | FiveThirtyEight and
elections, Who’s ahead in the mid-term race
to house prices and
coups d’état How to predict a coup Academics have built models to assess the probability of a putsch
Bad data, poor modelling, and insufficient communication are all real threats to even seasoned journalists
http://blogs.lse.ac.uk/mediapolicyproject/2018/03/02/journalism-and-artificial-intelligence-some-notes/
3.3 Programas, ejemplos, ventajas y limitaciones
WSJ R&D: bringing data science and AI capabilities to the newsroom
The Wall Street Journal Research & Development unit was launched in March 2018.
The output of the group includes newsgathering tools, data science driven stories and automation projects. Some recent efforts include a machine learning powered content analytics platform that analyses the Journal’s archive and helps journalists find editorial insights; an online tool that automatically turns college rankings data into text content and a story using computational journalism techniques to investigate Facebook’s data privacy sharing practices.
Develop AI tools to source information and story ideas through new types of data collection processes.
Help journalists explore opportunities to dynamically redefine content packages at scale.
Conduct research to identify technological trends that will impact the future of journalism.
A shared process: increasing internal engagement
«These smart tools can augment the work of our talented journalists, but they can’t replicate their journalistic instinct. Although technology is increasingly impactful in the modern media landscape, the editorial process will always be driven by humans,» Francesco Marconi, WSJ R&D Chief .
4 EL FIN DE LA VEROSIMILITUD LO QUE SIRVE PARA MENTIR SIRVE TAMBIÉN PARA DECIR LA VERDAD
Deep Fake y el retorno de Varela y Maturana
Fake news y el retorno de la aguja hipodermica
Guyot y el futuro de la informacion
#2 Leer e Informar – Filosofía de la Innovación
Verdadero o Falso: jugá y unite a la batalla contra la desinformación
5.COMO APRENDEMOS
Las neurociencias y la insuficiendia de la IA local
que pasa con la general?
Muchas otras referencias a investigar
https://www.wired.com/story/journalism-isnt-dying-its-returning-its-roots/
http://cplusj.org/keynote-speaker-lisa-gibbs
https://www.tandfonline.com/doi/abs/10.1080/21670811.2017.1289819
https://www.tandfonline.com/doi/abs/10.1080/17512786.2017.1320773?journalCode=rjop20
https://www.tandfonline.com/doi/abs/10.1080/21670811.2014.976412?journalCode=rdij20
https://www.tandfonline.com/doi/abs/10.1080/21670811.2016.1209083?journalCode=rdij20
https://www.tandfonline.com/doi/abs/10.1080/21670811.2017.1345643?journalCode=rdij20
https://medium.com/journalism-innovation/the-age-of-algorithms-needs-editors-d70ef1f580e3
http://www.journals.uio.no/index.php/TJMI/article/view/2420
https://thewholestory.solutionsjournalism.org/complicating-the-narratives-b91ea06ddf63
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Matchear
Corinna Underwood Automated Journalism – AI Applications at New York Times, Reuters, and Other Media Giants
Artificial intelligence in news media is being used in new ways from speeding up research to accumulating and cross-referencing data and beyond.
An Overview of Findings in Automated Journalism
AI is enhancing the newsroom in the following ways:
• Streamlining media workflows: AI enables journalists to focus on what they do best: reporting as illustrated by BBC’s Juicer.
• Automating mundane tasks: An application such as Reuter’s News Tracer can track down breaking news, so that journalists are not tied down to grunt work.
• Crunching more data: Research can be performed much faster, as shown by The New York Times Research and Development Lab’s Editor application.
• Digging out media insights: Information can be correlated quickly and efficiently, such as The Washington Post’s Knowledge Map.
• Eliminating fake news: Fact checking is speedy and reliable. Facebook is using AI to detect word patterns that may indicate a fake news story.
• Generating outputs: Machines can put together reports and stories from raw data, such as Narrative Science’s, Quill platform, which turns data into intelligent stories.
The New York Times – Semantic Discovery, Comment Monitoring
Video Vimeo IMG
The Perspective API tool developed by Jigsaw (part of Google’s parent company Alphabet) IMG
BBC News Labs – Semantic Discovery
Juicer news semantic analysis
Reuters – Data Visualization
https://www.graphiq.com
The Washington Post – Automated Journalism
Washington Post to Cover Every Major Race on Election Day With Help of Artificial Intelligence
Heliograf, a data-crunching program built in-house, will automatically update stories as results roll in on Election Day
https://www.wsj.com/articles/washington-post-to-cover-every-major-race-on-election-day-with-help-of-artificial-intelligence-1476871202
Yahoo! Sports – Automated Journalism
https://www.wired.com/story/journalism-isnt-dying-its-returning-its-roots/
Journalism Isn’t Dying. It’s Returning to Its Roots
Three leading digital outlets—BuzzFeed, the Huffington Post, and Vice—announced layoffs that left many accomplished journalists unemployed. The fingers of blame quickly pointed to the great bogeymen of our media age—Facebook and Google—and warned about a threat to democracy.
They’d have no notion of journalistic «objectivity,» and would find the entire undertaking futile (and likely unprofitable, but more on that soon).
What is dying, perhaps, is that flavor of «objective» journalism that purports to record an unbiased account of world events
Computation + Journalism 2019 was held at the University of Miami, FL on Feb 1-2. See the 2020 site here.
Hossein Derakhshan
The core crisis of journalism is not about business models, quality, ethics, or trust. It is that news, the heart of journalism, is dying. It is losing its cultural relevance after almost two centuries — and thereby its commodity value.
News was a cultural invention,
Suddenly the world grew bigger after the break away from time and space which telegraph caused.
«News both forms and reflects a particular ‘hunger for experience,’ a desire to do away with the epic, heroic, and traditional in favor of the unique, original, novel, new—news.»
news has lost its monopoly on the sense of globality it once generated.
in reaction to speedy and at times excessive and careless global movement of capital, goods, and work, more and more disillusioned people come to walk the opposite direction — from the global toward the local
News used to be the main source of daily drama for the expanding literate class.
with the invention of cinema, television, video games, YouTube, Twitter, and Netflix there are many other things than news to discuss at breakfast table — if there is still such a thing.
The result is a bifurcation: 1) A short-form journalism which is growingly produced by news makers than news outlets directly (tweets by politicians or local police or authorities) and few people are ready to pay for them; 2) A long-form narrative journalism in text, audio, and video which are symbolized by non-fiction books, documentary podcasts, and video documentaries — all with steady or growing market appeal.
The truth is that the news is dying, but journalism will not — and should not. If, as Carey once urged, journalism and democracy are synonymous with public conversation, the crisis of journalism can only be a reflection of the crisis of democracy.
https://www.niemanlab.org/collection/predictions-2019/
https://www.tandfonline.com/doi/abs/10.1080/21670811.2017.1289819
When Reporters Get Hands-on with Robo-Writing
Professionals consider automated journalism’s capabilities and consequences
The availability of data feeds, the demand for news on digital devices, and advances in algorithms are helping to make automated journalism more prevalent. This article extends the literature on the subject by analysing professional journalists’ experiences with, and opinions about, the technology. Uniquely, the participants were drawn from a range of news organizations—including the BBC, CNN, and Thomson Reuters—and had first-hand experience working with robo-writing software provided by one of the leading technology suppliers. The results reveal journalists’ judgements on the limitations of automation, including the nature of its sources and the sensitivity of its «nose for news». Nonetheless, journalists believe that automated journalism will become more common, increasing the depth, breadth, specificity, and immediacy of information available. While some news organizations and consumers may benefit, such changes raise ethical and societal issues and, counter-intuitively perhaps, may increase the need for skills—news judgement, curiosity, and scepticism—that human journalists embody.
https://www.tandfonline.com/doi/abs/10.1080/17512786.2017.1320773?journalCode=rjop20
Automated Journalism 2.0: Event-driven narratives
From simple descriptions to real stories
This article introduces an exploratory computational approach to extending the realm of automated journalism from simple descriptions to richer and more complex event-?driven narratives, based on original applied research in structured journalism. The practice of automated journalism is reviewed and a major constraint on the potential to automate journalistic writing is identified, namely the absence of data models sufficient to encode the journalistic knowledge necessary for automatically writing event-driven narratives. A detailed proposal addressing this constraint is presented, based on the representation of journalistic knowledge as structured event and structured narrative data. We describe a prototyped database of structured events and narratives, and introduce two methods of using event and narrative data from the prototyped database to provide journalistic knowledge to a commercial automated writing platform. Detailed examples of the use of each method are provided, including a successful application of the approach to stories about car chases, from initial data reporting through to automatically generated text. A framework for evaluating automatically generated event-driven narratives is proposed, several technical and editorial challenges to applying the approach in practice are discussed, and several high-level conclusions about the importance of data structures in automated journalism workflows are provided.
https://www.tandfonline.com/doi/abs/10.1080/21670811.2014.976412?journalCode=rdij20
The Robotic Reporter
Automated journalism and the redefinition of labor, compositional forms, and journalistic authority
Among the emergent data-centric practices of journalism, none appear to be as potentially disruptive as «automated journalism.» The term denotes algorithmic processes that convert data into narrative news texts with limited to no human intervention beyond the initial programming choices. The growing ability of machine-written news texts portends new possibilities for an expansive terrain of news content far exceeding the production capabilities of human journalists. A case study analysis of the pioneering automated journalism provider Narrative Science and journalists’ published reactions to its services reveals intense competition both to imagine an emergent journalism landscape in which most news content is automated and to define how this situation creates new challenges for journalists. What emerges is a technological drama over the potentials of this emerging news technology concerning issues of the future of journalistic labor, the rigid conformity of news compositional forms, and the normative foundation of journalistic authority. In these ways, this study contends with the emergent practice of automated news content creation both in how it alters the working practices of journalists and how it affects larger understandings of what journalism is and how it ought to operate.
https://www.tandfonline.com/doi/abs/10.1080/21670811.2016.1209083?journalCode=rdij20
I, Robot. You, Journalist. Who is the Author?
The broadening reliance on algorithms to generate news automatically, referred to as «automated journalism» or «robot journalism», has significant practical, sociopolitical, psychological, legal and occupational implications for news organizations, journalists and their audiences. One of its most controversial yet unexplored aspects is the algorithmic authorship. This paper integrates a multidisciplinary theoretical framework of algorithmic creativity, bylines and full disclosure policies, legal views on computer-generated works, and an empirical study of attribution regimes in pioneering organizations that produce journalistic content automatically. Fieldwork included quantitative content analysis of automated stories on 12 websites and interviews with key figures from seven of the organizations that agreed to be interviewed, despite the general reluctance of news organizations to be identified with such an endeavor. The study detects major discrepancies between the perceptions of authorship and crediting policy, the prevailing attribution regimes and the scholarly literature. To mitigate these discrepancies, we offer a consistent and comprehensive crediting policy that sponsors public interest in automated news
https://www.tandfonline.com/doi/abs/10.1080/21670811.2017.1345643?journalCode=rdij20
Automated News
Better than expected?
We conducted two experiments to study people’s prior expectations and actual perceptions of automated and human-written news. We found that, first, participants expected more from human-written news in terms of readability and quality; but not in terms of credibility. Second, participants’ expectations of quality were rarely met. Third, when participants saw only one article, differences in the perception of automated and human-written articles were small. However, when presented with two articles at once, participants preferred human-written news for readability but automated news for credibility. These results contest previous claims according to which expectation adjustment explains differences in perceptions of human-written and automated news.
http://blogs.lse.ac.uk/mediapolicyproject/2018/06/25/using-artificial-intelligence-in-news-intelligently-towards-responsible-algorithmic-journalism/
Using artificial intelligence in news intelligently: towards responsible algorithmic journalism
how to make use of artificial intelligence in a way that saves costs and increases users’ experience, without compromising on quality or the provision of diverse and relevant news.
the 2018 Amsterdam Symposium on News Personalisation,
the impact of news personalisation on editorial values
for which values should news personalisation algorithms be optimised?
empowerment.
several implementation strategies, such as conversational and dynamic profiles, or more user-driven modes of personalisation.
Deciding that a personalisation system should recommend ‘relevant’ articles triggers hard questions like which criteria determine relevancy, how we can measure users’ interest beyond what they click into, and how to weigh users’ interests against what editors feel is relevant.
The FairNews project, in which the University of Amsterdam, TU Delft and Dutch daily De Volkskrant work together to ensure algorithmic recommendations are implemented in a fair way, is a good example.
https://medium.com/journalism-innovation/the-age-of-algorithms-needs-editors-d70ef1f580e3
The age of the algorithm needs editors
Cathy O’Neil declares that we are living in the age of the algorithm. Artificial intelligence is quietly revolutionizing every industry, from media and marketing to healthcare, transportation, financial services and beyond.
Spending on A.I. and machine learning is expected to grow from $12 billion in 2017 to $57.6 billion by 2021
PwC, AI’s potential contribution to the global economy could be $15.7 trillion by 2030.
to spread disinformation and influence elections around the world. Programmatic advertising was found to be funding terrorism. Search engines are reinforcing racism. Big data is increasing inequality. Facial recognition software discriminates based on race and gender. A.I. is being used to police, profile and punish the poor. And predictive software used in courts around the country is biased against black defendants.
FALTA IMAGEN
How can editors help guard against bias? How can they push for algorithmic transparency and oversight? How can they ensure that respect for the individual is accounted for in these systems? How can they help anticipate ethical implications?
current engineering ethics education is not effective.
«Artificial Intelligence: Practice and Implications for Journalism
editing is a form of data analysis. Editors are skilled at putting information in context, assessing the accuracy of data and weeding out bias. They view issues from multiple angles, connect the dots and uncover human stories in complex systems. Editors, like engineers, are talented problem solvers.
Accountable Journalism database, there are more than 400 codes of media ethics around the world. Though they vary by practice and scope, all have five values in common: accuracy, independence, impartiality, humanity and accountability.
Algorithmic Accountability Reporting: On the Investigation of Black Boxes.»
algorithmic ombudsman,»
http://www.journals.uio.no/index.php/TJMI/article/view/2420
https://reutersinstitute.politics.ox.ac.uk/
our-research/tweet-first-verify-later-how-real-time-information-changing-coverage-worldwide-crisis
Tweet first , verify later? How real-time information is changing the coverage of worldwide crisis events
https://reutersinstitute.politics.ox.ac.uk/sites/default/files/research/files/Tweet%2520first%2520%252C%2520verify%2520later%2520How%2520real-time%2520information%2520is%2520changing%2520the%2520coverage%2520of%2520worldwide%2520crisis%2520events.pdf
https://thewholestory.solutionsjournalism.org/complicating-the-narratives-b91ea06ddf63
Complicating the Narratives
What if journalists covered controversial issues differently — based on how humans actually behave when they are polarized and suspicious?
Your Brain in Conflict
The Conversation Whisperer
1. Amplify Contradictions
2. Widen the Lens
3. Ask Questions that Get to People’s Motivations
• What is oversimplified about this issue?
• How has this conflict affected your life?
• What do you think the other side wants?
• What’s the question nobody is asking?
• What do you and your supporters need to learn about the other side in order to understand them better?
4. Listen more, and better
5. Expose People to the Other Tribe
6. Counter Confirmation Bias (Carefully)
https://medium.com/m/global-identity?redirectUrl=https://thewholestory.solutionsjournalism.org/complicating-the-narratives-b91ea06ddf63
Videos
Alex Edmands
http://bit.ly/2PbgUv3
Jorge Ramos
http://bit.ly/2RicWnk
Joan Blades
http://bit.ly/33QTJvH
Jeff Leek
http://bit.ly/2P9Fo87
Christiane Amanpour
http://bit.ly/2DFJU8T
Predictions for journalism
https://www.niemanlab.org/collection/predictions-2019/
Facts weaponized
Vertical storytelling
slow slog
Correcting corrections
Data Journalism
Pageviews to impacts
Risks of AI – What Researchers Think is Worth Worrying About
Risks of AI – What Researchers Think is Worth Worrying About
The year 2015 might be seen as the year that «artificial intelligence risk» or «artificial intelligence danger» went mainstream (or close to it). With the founding of Elon Musk’s Open AI and The Leverhulme Centre for the Future of Intelligence; the increased attention on the Future of Life Institute and Oxford’s Future of Humanity Institute; and a flurry of attention around celebrity comments around AI dangers (including the now well-known statements of Bill Gates and Elon Musk),
More to fear about human beings than AI?
The risks brought about by near-term AI may turn out to be the same risks that are already inherent in our society. Automation through AI will increase productivity, but won’t improve our living conditions if we don’t move away from a labor/wage based economy. It may also speed up pollution and resource exhaustion, if we don’t manage to install meaningful regulations.»
«With AI you can sort of enable communism to work, maybe not so much as an economic theory, but at least as a political theory. So it is definitely a Leninist thing. And then, it is literally communist because China loves AI; it hates crypto»